Blar i Kavliinstitutt for nevrovitenskap på tidsskrift "Entropy"

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    • Life on the edge: Latching dynamics in a Potts neural network 

      Kang, Chol Jun; Naim, Michelangelo; Boboeva, Vezha; Treves, Alessandro (Journal article; Peer reviewed, 2017)
      We study latching dynamics in the adaptive Potts model network, through numerical simulations with randomly and also weakly correlated patterns, and we focus on comparing its slowly and fast adapting regimes. A measure, ...

    • Minimum Description Length codes are critical 

      Cubero, Ryan John Abat; Marsili, Matteo; Roudi, Yasser (Journal article; Peer reviewed, 2018)
      In the Minimum Description Length (MDL) principle, learning from the data is equivalent to an optimal coding problem. We show that the codes that achieve optimal compression in MDL are critical in a very precise sense. ...

    • Minimum Description Length Codes Are Critical 

      Cubero, Ryan John Abat; Marsili, Matteo; Roudi, Yasser (Journal article; Peer reviewed, 2018)
      In the Minimum Description Length (MDL) principle, learning from the data is equivalent to an optimal coding problem. We show that the codes that achieve optimal compression in MDL are critical in a very precise sense. ...

    • The capacity for correlated semantic memories in the cortex 

      Boboeva, Vezha; Brasselet, Romain; Treves, Alessandro (Journal article; Peer reviewed, 2018)
      A statistical analysis of semantic memory should reflect the complex, multifactorial structure of the relations among its items. Still, a dominant paradigm in the study of semantic memory has been the idea that the mental ...

    • The Stochastic Complexity of Spin Models: Are Pairwise Models Really Simple? 

      Beretta, Alberto; Battistin, Claudia; de Mulatier, Clelia; Mastromatteo, Iacopo; Marsili, Matteo (Journal article; Peer reviewed, 2018)
      Models can be simple for different reasons: because they yield a simple and computationally efficient interpretation of a generic dataset (e.g., in terms of pairwise dependencies)—as in statistical learning—or because they ...